v2.2.4.Nbeal.Labeling
D. Ford Hannum Jr.
9/28/2020
knitr::opts_chunk$set(echo = TRUE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(ggrepel)## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
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## locale:
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] ggrepel_0.8.2 limma_3.44.3
## [3] tidyr_1.1.1 dplyr_1.0.2
## [5] SingleR_1.2.4 MAST_1.14.0
## [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
## [9] DelayedArray_0.14.1 matrixStats_0.56.0
## [11] Biobase_2.48.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [17] data.table_1.13.0 ggplot2_3.3.2
## [19] Seurat_3.2.0
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## loaded via a namespace (and not attached):
## [1] AnnotationHub_2.20.1 BiocFileCache_1.12.1
## [3] plyr_1.8.6 igraph_1.2.5
## [5] lazyeval_0.2.2 splines_4.0.2
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## [11] magrittr_1.5 memoise_1.1.0
## [13] tensor_1.5 cluster_2.1.0
## [15] ROCR_1.0-11 globals_0.12.5
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Labeling the Nbeal clusters, to figure out where they are getting moved to in the integrated data. The goal here is to better label the clusters of the integrated dataset with higher confidence
Nbeal Data Only
cnt.data <- Read10X(data.dir = './data/Experiment2/filtered_feature_bc_matrix/')
cnt <- CreateSeuratObject(counts = cnt.data, project = 'Nbeal', min.cells = 3, min.features = 200)
# Getting the HTOs
nbeal_hto <- read.table('./data/Experiment2/hto_labels.txt')
nbeal_hto <- nbeal_hto[nbeal_hto$V2 %in% c('HTO3','HTO4'),]
nbeal_hto$condition <- ifelse(nbeal_hto$V2 == 'HTO3', 'Nbeal_cntrl', 'enrNbeal_cntrl')
nbeal_hto$cell <- paste0(nbeal_hto$V1, '-1')
# summary(nbeal_hto$cell %in% colnames(cnt))
cnt <- cnt[,colnames(cnt) %in% nbeal_hto$cell]
# Making sure the cell order is maintained between the two dataframes, so I can
# just add the condition to the meta data
#summary(rownames(wbm@meta.data) == htos$Barcode)
# Adding the condition to the meta data
cnt@meta.data$Condition <- nbeal_hto$condition
cnt[['percent.mt']] <- PercentageFeatureSet(cnt, pattern = '^mt')
less.cnt <- subset(cnt, subset = nFeature_RNA > 500 & percent.mt < 10)less.cnt <- NormalizeData(less.cnt, verbose = F)
less.cnt <- FindVariableFeatures(less.cnt,
selection.method = 'vst',
nfeatures = 2000,
verbose = F)
less.cnt <- ScaleData(less.cnt, verbose = F)
less.cnt <- RunPCA(less.cnt, features = VariableFeatures(less.cnt))
ElbowPlot(less.cnt)
# choosing 15 PCs
less.cnt <- FindNeighbors(less.cnt, dims = 1:15)
res <- seq(0,1, by = 0.05)
clstrs <- c()
for (i in res){
x <- FindClusters(less.cnt, resolution = i, verbose = F)
clstrs <- c(clstrs, length(unique(x$seurat_clusters)))
}
plot(res,clstrs)
# Going with .2 and .7
less.cnt <- FindClusters(less.cnt, resolution = .2, verbose = F)
less.cnt <- FindClusters(less.cnt, resolution = .7, verbose = F)
less.cnt <- RunUMAP(less.cnt, dims = 1:15)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session# DimPlot(less.cnt, reduction = 'umap', label = T, repel = T) + NoLegend() + ggtitle ('Resolution 0.7')
DimPlot(less.cnt, reduction = 'umap', label = T, repel = T, group.by = 'RNA_snn_res.0.2') +
NoLegend() + ggtitle ('Resolution 0.2')
Alt Text
Going to go with clustering resolution 0.2, which is what is displayed above. Of key is cluster 3 which becomes clusters 6 (MEP/Mast), 7 (CMP), part of 10 (Erythrocytes) and 12 (MKs); in the integrated dataset, see v2.1.3 Cluster Centroid for more details.
Nbeal Data in Integrated Data Set
# Calling the Seurat variable wbm instead of comb.int which is what it was previously
wbm <- readRDS('./data/v2/lesser.combined.integrated.rds')
wbm$State <- wbm$Condition
wbm$Condition <- ifelse(grepl('enr', wbm$Condition), 'Enriched', 'Not enriched')
wbm$Experiment <- ifelse(grepl('Mpl', wbm$State), 'Mpl',
ifelse(grepl('Migr', wbm$State), 'Migr1', 'Control'))
sumry <- read.table('./data/v2/summary_naming.tsv', header = T, sep = '\t')
# sumry
# sumry2 <- sumry
# sumry$final2 <- sumry$final
# sumry$final2[c(3,8)] <- c('?GMP','?CMP')
# write.table(sumry,'./data/v2/summary_naming.tsv', quote = F, row.names = F, sep = '\t')
lbls <- c('Gran-1','Gran-2','?GMP','Bcell-1','Gran-3','Monocyte','?MEP/MAST','?CMP','Macrophage',
'Bcell-2','Erythrocyte','Tcell','MK','Bcell-3','Bcell-4')
new_levels <- lbls
names(new_levels) <- levels(wbm)
#new_levels
wbm <- RenameIdents(wbm, new_levels)
wbm$new_cluster_IDs <- Idents(wbm)
DimPlot(wbm, reduction = 'umap', label = T, repel = T) + NoLegend()
Now I am going to do a similar labeling as in file v2.2.Labeling, which is what was used to generate the cell types shown, but just in the NBeal-control cells.
##
## Gran-1 Gran-2 ?GMP Bcell-1 Gran-3 Monocyte
## 403 335 149 236 167 148
## ?MEP/MAST ?CMP Macrophage Bcell-2 Erythrocyte Tcell
## 18 26 33 103 35 47
## MK Bcell-3 Bcell-4
## 131 84 33SingleR
# Loading up reference datasets
m.ref.immgen <- ImmGenData()
m.ref.mus <- MouseRNAseqData()
ref <- list(m.ref.immgen, m.ref.mus)
ref.label <- list(m.ref.immgen$label.main, m.ref.mus$label.main)
# Creating a sc experiment from our seurat object
SCnbeal <- as.SingleCellExperiment(nbeal)
# Predicting the Cluster label
pred_cluster <- SingleR(test = SCnbeal,
ref = ref,
labels = ref.label,
method = 'cluster',
clusters = SCnbeal$new_cluster_IDs)
# Predicting individual cell labels
pred_cell <- SingleR(test = SCnbeal,
ref = ref,
labels = ref.label,
method = 'single')Cluster Labels
# pred_cluster$scores
#
# pred_cluster$labels
#
# pred_cluster$pruned.labels
#
# pred_cluster$orig.results
pred_scores_cluster <- pred_cluster$scores
# Deleting columns without any values
pred_scores_cluster <- as.data.frame(pred_scores_cluster[,colSums(is.na(pred_scores_cluster)) != nrow(pred_scores_cluster)])
rownames(pred_scores_cluster) <- lbls
colnames(pred_scores_cluster)[8:13] <- paste0(colnames(pred_scores_cluster)[8:13],'-2')
pred_scores_cluster <- gather(pred_scores_cluster, Cell.Type, Score, factor_key = T)
pred_scores_cluster$seurat.cluster <- rep(lbls, 13)
pred_scores_cluster$ref <- ifelse(is.na(tstrsplit(pred_scores_cluster$Cell.Type,'-')[[2]]), 'ref1','ref2')
pred_scores_cluster$seurat.cluster2 <- as.factor(pred_scores_cluster$seurat.cluster)
pred_scores_cluster$score2 <- ifelse(is.na(pred_scores_cluster$Score), 0, pred_scores_cluster$Score)
ggplot(data = pred_scores_cluster, aes(y = Cell.Type, x = seurat.cluster2,
fill = ref, alpha = score2)) +
geom_tile() +
theme(axis.text.x = element_text(angle = 45, vjust = .5)) +
scale_fill_manual(values = c('red','blue'))
For the most part what we would expect and aligns with the labels generated from the integrated analysis (x-axis labels).
# pulling out the information we need
pred_cell_score <- pred_cell[,c('pruned.labels','reference')]
# Adding the seurat cluster to the cell
#summary(rownames(pred_cell_score) == rownames(wbm@meta.data))
pred_cell_score$cluster <- nbeal$new_cluster_IDs
cell_score <- as.data.frame(table(paste0(pred_cell_score$pruned.labels,'-',
pred_cell_score$reference),
pred_cell_score$cluster))
colnames(cell_score) <- c('Cell Type','Cluster','Count')
# ggplot(cell_score[cell_score$Cluster == 'MK',], aes(x = Cluster, y = Count, fill = `Cell Type`)) +
# geom_bar(stat = 'identity', position = position_dodge()) +
# theme_bw() +
# geom_text(stat = 'identity', aes(label = Count),
# position = position_dodge(width = .9),
# vjust = -.1, size = 2.5)
cell_score$cluster_count <- NA
for (i in unique(cell_score$Cluster)){
cell_score[cell_score$Cluster ==i,]$cluster_count <-
sum(cell_score[cell_score$Cluster ==i,]$Count)
}
cell_score$count_per <- round(cell_score$Count/cell_score$cluster_count,2)*100
# ggplot(cell_score, aes (x = Cluster, y = count_per, fill = `Cell Type`)) +
# geom_bar(stat = 'identity', position = position_dodge()) +
# theme_bw() +
# geom_text(stat = 'identity', aes(label = count_per),
# position = position_dodge(width = .9),
# vjust = -.1, size = 2.5)
ggplot(cell_score, aes(x = Cluster, y = `Cell Type`, fill = count_per)) +
geom_tile() +
scale_fill_gradient2(low = 'white', mid = 'red', high = 'darkred',
midpoint = 50)
cell_score$ref <- grepl('1',cell_score$`Cell Type`)
cell_score$ref <- ifelse(cell_score$ref == T, 'ref1','ref2')
ggplot(cell_score, aes(x = Cluster, y = `Cell Type`, alpha = count_per,
fill = ref)) +
scale_fill_gradient2(low = 'white', mid = 'red', high = 'darkred',
midpoint = 50) +
geom_tile() +
scale_fill_manual(values = c('red','blue')) +
theme(axis.text.x = element_text(angle = 90, vjust = .2, hjust = .95))
Similar results once again. Still curious is why the ?MEP/MAST relate so highly to basophils.
Marker Gene Expression
Cell type specific marker gene expression. Genes were added to the list in two different ways: canonical markers that are well known in the field, and genes that distinguished clusters and were found to play a key role in specific cells.
Literature for Markers: Previously used
Ighd: immunoglobulin heavy constant delta. Seems to clearly be expressed by B-cells, but still working on a good reference.
Gata2: From Krause paper: a transcription factor required for both lineages but bind in different combinations ref
Cd68: a human macrophage marker ref. A more general ref
Vcam1: found papers using Vcam1+ monocytes, but haven’t found a great reference.
Alas2: an erythroid-specfiic 5-aminolevulinate synthase gene ref
Gata3: plays a role in the regulation of T-cells ref
Vwf and Itga2b: I figure the reference would best be left to y’all.
New Markers
Ly6g: from website it plays a role in monocyte, granulocyte, and neutrophil
Ngp: from uniprot “Expressed in myeloid bone marrow cells. Expressed in neutrophilic precursors (at protein level) (PubMed:8749713). Expressed in myeloid bone marrow cells (PubMed:21518852)”
Mmp8: neutrophil/lymphocyte collagenase link
marker.genes <- rev(c('Itga2b','Vwf','Gata3','Alas2','Vcam1','Cd68','Gata2','Ighd'))
DotPlot(wbm, features = marker.genes) +
ylab ('Cell Cluster') + xlab ('Marker Genes') +
theme(text = element_text(size = 10, family = 'sans'),
axis.text.x = element_text(angle = 45,
vjust = .5, size = 10, family = 'sans'),
axis.text.y = element_text(family = 'sans', size = 10),
axis.title = element_text(family = 'sans', size = 12))
otros.marker.genes <- rev(c('Cebpe','Fcnb'))
DotPlot(wbm, features = otros.marker.genes) +
ylab ('Cell Cluster') + xlab ('Marker Genes') +
theme(text = element_text(size = 10, family = 'sans'),
axis.text.x = element_text(angle = 45,
vjust = .5, size = 10, family = 'sans'),
axis.text.y = element_text(family = 'sans', size = 10),
axis.title = element_text(family = 'sans', size = 12))
new.markers <- c('Mcpt8','Prss34','Kit','Jchain','Hmgb1', 'Vpreb3','Igkc','Ighm')
hspc.markers <- c('SCA-1', 'Cd38','Thy1','Kit')
DotPlot(wbm, features = hspc.markers) +
ylab ('Cell Cluster') + xlab ('Marker Genes') +
theme(text = element_text(size = 10, family = 'sans'),
axis.text.x = element_text(angle = 45,
vjust = .5, size = 10, family = 'sans'),
axis.text.y = element_text(family = 'sans', size = 10),
axis.title = element_text(family = 'sans', size = 12))## Warning: Could not find Cd38 in the default search locations, found in RNA assay
## instead## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: SCA-1
DotPlot(wbm, features = c(otros.marker.genes,new.markers,marker.genes)) +
ylab ('Cell Cluster') + xlab ('Marker Genes') +
theme(text = element_text(size = 10, family = 'sans'),
axis.text.x = element_text(angle = 45,
vjust = .5, size = 10, family = 'sans'),
axis.text.y = element_text(family = 'sans', size = 10),
axis.title = element_text(family = 'sans', size = 12))
It seems that the lymphoid cells, monocyte and macrophage clusters are easily identifiable but the other questions have remaining questions.
What type of granulocyte are gran-1 and gran-2?
Are ?GMP and ?CMP truly a progenitor state
Are there better markers for Erythrocytes
Are the hspcs mixed in with MKs? Kit is of importance when labeling HSPCs but that MK cluster highest in Kit also is the only cluster somewhat widely expressing Vwf.
More specific markers for different stages of CMPs to helpfully clear some things up.
Why is there a cluster expressing both MEP and Mast cell markers so strongly?
MORE WORK
Human WBM Comparison
hwbm_ex <-Read10X(data.dir = './data/hum_ref_wbm/GSE120221_RAW/GSM3396161/')
hwbm <- CreateSeuratObject(counts = hwbm_ex, project = 'hwbm', min.cells = 3, min.features = 200)## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')hwbm_cell_labels <- read.csv('./data/hum_ref_wbm/celltype.csv')
#hwbm_cell_labels
hwbm_cell_labels$cell <- tstrsplit(hwbm_cell_labels$X, '_', keep = 2)[[1]]
hwbm_cell_labels$exp <- tstrsplit(hwbm_cell_labels$X, "_", keep = 1)[[1]]
hwbm_cell_labels <- hwbm_cell_labels[hwbm_cell_labels$exp == 'A',]
hwbm_cell_labels$cell <- paste0(hwbm_cell_labels$cell, '-1')
hwbm[['percent.mt']] <- PercentageFeatureSet(hwbm, pattern = "^MT-")
cells_to_keep <- colnames(hwbm)[colnames(hwbm) %in% hwbm_cell_labels$cell]
hwbm <- subset(hwbm, cells = cells_to_keep)
genes_to_keep <- rownames(hwbm)[rownames(hwbm) %in% rownames(wbm)]
hwbm <- subset(hwbm, features = genes_to_keep)
hwbm <- NormalizeData(hwbm, normalization.method = 'LogNormalize', scale.factor = 10000)
hwbm <- ScaleData(hwbm, features = rownames(hwbm))
nbeal2 <- subset(nbeal, features = genes_to_keep)#summary(hwbm_cell_labels$cell == rownames(hwbm@meta.data))
hwbm@meta.data$cell_id <- hwbm_cell_labels$type
Idents(hwbm) <- hwbm$cell_id
av_wbm <- AverageExpression(nbeal2)$RNA
av_hwbm <- AverageExpression(hwbm)$RNA
av <- cbind(av_wbm, av_hwbm)
av_cor <- cor(av, method = 'kendall')
av_cor2 <- as.data.frame(av_cor)
av_cor2$row <- rownames(av_cor2)
colnames(av_cor2)[1:15] <- paste0('Cluster',0:14)
rownames(av_cor2)[1:15] <- paste0('Cluster',0:14)
av_cor2$row <- rownames(av_cor2)
# gather(av_cor2, row, cor, Cluster0:Cluster14, factor_key = T)
av_cor2 <- reshape(av_cor2, direction = 'long',
varying = list(names(av_cor2)[1:34]),
v.names = 'Correlation',
idvar = c('row'),
timevar = 'CT2',
times = names(av_cor2)[1:34])
av_cor2$row <- factor(av_cor2$row, levels = unique(av_cor2$row))
av_cor2$CT2 <- factor(av_cor2$CT2, levels = unique(av_cor2$CT2))
ggplot(av_cor2, aes(x = row, y = CT2, fill = Correlation)) +
geom_tile() +
theme_bw() +
scale_fill_gradient2(high = 'darkred', low = 'white', mid = 'red',,
midpoint = 0.5, limit = c(0,1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
lvls <- levels(av_cor2$row)
av_cor3 <- av_cor2[av_cor2$row %in% lvls[1:15] & av_cor2$CT2 %in% lvls[16:34],]
ggplot(av_cor3, aes(x = row, y = CT2, fill = Correlation)) +
geom_tile() +
theme_bw() +
scale_fill_gradient2(high = 'darkred', low = 'white', mid = 'red',,
midpoint = 0.5, limit = c(0,1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
av_cor3$row_labels <- rep(lbls,19)
ggplot(av_cor3, aes(x = row_labels, y = CT2, fill = Correlation)) +
geom_tile() +
theme_bw() +
scale_fill_gradient2(high = 'darkred', low = 'white', mid = 'red',,
midpoint = 0.5, limit = c(0,1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))